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Record W2787438578 · doi:10.3917/lautr.054.0272

Faire entrer le plurilinguisme dans les équipes cliniques

2018· article· fr· W2787438578 on OpenAlexaboutno aff
Yvan Leanza

Bibliographic record

VenueL Autre · 2018
Typearticle
Languagefr
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceSociologyArt

Abstract

fetched live from OpenAlex

À partir du cadre théorique de la niche d’activité professionnelle et d’études effectuées au Québec, un portrait des pratiques et représentations du travail avec interprètes dans les soins est dressé. Ce portrait est d’abord fait pour les contextes non sensibles aux dimensions linguistiques et culturelles des soins. Il est ensuite dépeint plus en détail avec deux études menées en contextes sensibles. Cette comparaison permet de mettre en évidence les niveaux de contexte qu’il est nécessaire de prendre en compte pour l’intégration des interprètes dans les équipes de soins et pour établir une collaboration interprète-praticien de qualité : politique, institutionnelle (qui inclut les dimensions structurelle et collaborative), de formation et déontologique. Pour chacun d’eux, les conditions idéales pour établir une collaboration interprète-praticien-usager constructive sont décrites.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.191
Threshold uncertainty score0.380

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0140.043
Scholarly communication0.0130.012
Open science0.0020.011
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0110.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.439
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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